Software Developer

Job Title: Machine Learning Engineer, Automated Scoring Team  

Location: Remote - US  

  

About Pearson’s Automated Scoring Team 

As the world's learning company, Pearson helps people make more of their lives through learning. We use our knowledge, passion, and reach to tackle the big problems in education and inspire a love of learning that lasts a lifetime. That is why we need smart people like you. Together, we can transform education and provide boundless opportunities for billions of learners worldwide.   

The Automated Scoring team develops machine learning-based models that analyze tens of millions of learner exam responses each year. Our technology is unique and meaningful, providing results quickly on student performance on standardized tests. The Machine Learning Engineer will join Pearson’s Automated Scoring Team to provide support for the administration of Pearson’s automated scoring programs and support the execution of initiatives to innovate and improve the delivery of Pearson's automated scoring technologies. This role will report to and work closely with the Director of Automated Scoring, but it will also support program managers, quality assurance automation engineers, psychometricians, and various internal stakeholders to ensure the quality and reliability of our automated scoring systems.  

Machine Learning Engineer’s Duties & Responsibilities  

Listed below are the typical duties and responsibilities expected of an individual for the job title. 

• Train, evaluate, and deploy machine learning models tasked with scoring short answer and essay student responses to formative and summative test administrations from school districts nationwide 
• Monitor performance of deployed machine learning models to ensure consistent, fair, and unbiased scoring in real time and recalibrate deployed models as needed 
• Maintain, update, and improve code base used to train and deploy machine learning models 
• Evaluate historical model performance and conduct experiments exploring strategies to potentially improve team modeling techniques and approaches 
• Research and stay up-to-date on emerging technologies in the NLP space 

Qualifications  

Qualified individuals will be required to work with dynamic teams driven by project delivery goals. They should possess the drive to learn and continuously improve on work performance. They must also be detail-oriented and eager to work with peers in producing quality output. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.  

• 0-2 years professional experience as a software engineer or data scientist 
• Solid understanding of machine learning principles and current/emerging technologies 
• Strong coding & analytics skills including proficiency in Python and Linux commands 
• Understanding of or experience with deploying machine learning models into production environments 
• Familiarity with software engineering fundamentals (version control, object-oriented and functional programming, database and API access patterns, testing) 
• Passionate about agile software processes, data-driven development, reliability, and systematic experimentation 
• Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities 
• Curious and always learning habits of mind 
• Strong team-oriented approach to work, with excellent interpersonal and communication skills, both oral and written 
• Ability to work effectively as a member of a team in a collaborative environment 
• Demonstrated ability to manage multiple tasks and projects simultaneously 

Desirable

• Bachelor’s degree in a quantitative field (CS, EE, statistics, math, data science) 

  

Experiences That Will Set You Apart  

• Advanced degree in a quantitative field (CS, EE, statistics, math, data science) 
• Track record of producing machine learning models and production infrastructure at scale 
• Familiarity with traditional natural language processing (NLP) techniques and/or latest advancements in large language models (LLMs), generative AI, active learning and reinforcement learning 
• Strong experience with machine learning in non-NLP domains 
• Experience using containerized technologies such as Docker and/or Kubernetes 

Working location and travel  

This position is remote.  

Willingness to travel as necessary. 

 

Compensation at Pearson is influenced by a wide array of factors i